Work

Identification of defects on the inner wall of water supply pipes based on the optimized residual network

Year: 2024

Type: article

Abstract: ABSTRACT The urban water supply network is part of the infrastructure that sustains the economic and social functions of cities and regions. Timely inspection and maintenance of the network can effectively reduce resource wastage and prevent accidents. Traditional manual detection methods are inefficient and can be based on subjective judgments. A classification model based on an improved ResNet34 network has been proposed to classify and detect various types of corrosion on the inner walls of pipes under challenging conditions. The introduction of the attention mechanism and multi-scale feature fusion modules further improved the model's effectiveness in classifying defects within the inner walls of pipes. The model can detect various types of pipeline corrosion with a detection accuracy of 98.61%. This accuracy is significantly superior to that achieved with traditional models such as ResNet34, AlexNet, MobileNet, and VGGNet.

Source: Journal of Hydroinformatics

DOI: 10.2166/hydro.2024.206

Authors: Qian Zhao, Gaojuan Wang

Institution: Xi'an University of Science and Technology

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Clean water and sanitation, Industry, innovation and infrastructure

Open Access status: diamond

Funders: Scientific Research Plan Projects of Shaanxi Education Department, Key Science and Technology Program of Shaanxi Province